Historical Growth Rates Research Articles

Results of the Far East Mortgage Program for Consumers, Developers, and the Government

The volume of mortgage loans issued in eleven regions of the Far Eastern Federal District at the end of 2023 increased 2.5 times by the start of the Far Eastern Mortgage program in 2020. The most interesting result of the program is an increase in demand for primary real estate and a simultaneous decrease in the affordability of this housing. Since the start of the program, prices have increased by 116% vs. 26% while maintaining historical growth rates. By the end of 2021, the price of an apartment, including interest under the Far East Program, has become higher than that of an apartment while maintaining the historical rate of price appreciation under the family mortgage rate. The program has effectively made housing less affordable. The main conclusion is that for program participants (young families under 35), the most effective benefit of stimulating home buying is lower interest payments, not down payments. The main beneficiaries of rising prices and the scale of lending are developers. Considering the increased price of construction materials, the developers’ margin in the Far Eastern Federal District in 2023 increased to 58%. On the one hand, the government has achieved its goal of boosting the housing market in the Far East and has found an effective tool to encourage young families to buy real estate. This is evidenced by the extension of the program until 2030. On the other hand, several economic shocks led to a significant increase in the key interest rate and the accompanying growth of the program implementation costs.

Explosive increase and decrease in articles, citations, impact factor, and immediacy index during the COVID-19 pandemic: a bibliometric study

Purpose: This study investigated how Journal Citation Reports (JCR) metrics changed during the COVID-19 pandemic (2020–2022), with the aim of sharing this information with stakeholders in the publishing community.Methods: In total, 7,689 journals listed in the JCR-Science Citation Index Expanded (SCIE) from 2016 to 2022 were selected. Data were analyzed using pivot tables in Microsoft Excel. We calculated the compound annual growth rate to investigate changes in JCR-SCIE articles, citations, the journal impact factor, and the immediacy index during the COVID-19 period.Results: A marked increase was noted in the number of articles and citations during the COVID-19 pandemic from 2020 to 2022. This surge was primarily driven by a significant rise in COVID-19–related articles. Consequently, four JCR metrics exhibited a sharp increase in 2020, followed by an unusually steep decline in 2022. Articles, citations, and the journal impact factor reached their highest recorded levels in 2021, while the immediacy index saw its most significant growth and intense citation activity in 2020 before experiencing notable decreases in 2021 and 2022. Our findings indicate that there was an unprecedented and dramatic shift in these four JCR metrics during the COVID-19 period, with current trends suggesting a reversion to historical compound annual growth rate levels.Conclusion: The journal publishing and scientific communities should consider these explosive changes when applying JCR metrics to evaluate articles and endeavor to mitigate the adverse effects of the unusual concentration of articles and citations during the COVID-19 period. These results constitute valuable information to be shared among researchers and stakeholders within the journal publishing community.

Forecasting The Population of China From 2020 To 2025 Based on Random Forest and Linear Regression

The population dynamics of a country do play a vital role in its economic and political development. The COVID-19 epidemic has significantly affected the world's population. However, most people only care about the negative influence caused by COVID-19 but ignore the positive influence. This article uses two machine learning models, the random forest model, and the linear regression model, to predict the population change in China if there were no COVID-19 pandemic. With the predicted results, this article can compare the potential positive impact of the pandemic. This paper tries to fit two popular models, namely, Random Roest and Linear Regression to forecast the population of China from 2020 to 2025. The historical birth rate, death rate, and GDP growth rate of China are collected as features adding to the models to decline the error. For the Random Forest model, this paper set up an ensemble of decision trees to predict the future population of China. For the Linear Regression model, our features and population fit a linear connection. The findings of two models are compared in this article, and it is suggested that the random forest model is more suited for population forecasting. In addition, according to this study, the COVID-19 pandemic has some favorable effects on economic growth and birth rates. The article emphasizes the need to not only focus on the negative effects of the pandemic. Furthermore, the article points out that the linear regression model has poor fitting results for non-linear relationships. It suggests exploring more non-linear models for prediction and considering more influential parameters.

A robust model to estimate a firm’s average economic return

Theoretical studies question the ability of financial statement information to provide evidence about a firm’s economic performance, as the mathematical relation between a firm’s accounting and economic returns becomes intractable when economic depreciation is defined endogenously—as a function of the firm’s cash flow stream—and when its internal rate of return (IRR) and investment growth rate have not been constant. This paper derives a new equation, called the average internal rate of return (AIRR) estimation model, in which a firm’s AIRR is estimated as a function of its current-year accounting return, its current-year asset growth rate, a measure of accounting conservatism, and a term that identifies the firm’s historical investment growth rate trend. Because the model allows economic depreciation to be defined exogenously, non-constant returns and growth rates can be aggregated across investment cohorts, allowing a firm’s accounting return to be reconciled to its average economic return. One such exogenous schedule—called competitive market depreciation—is used to illustrate the model’s implications. The AIRR estimation model empowers analysts to identify the direction and magnitude of the potential difference between a firm’s accounting and economic return created by fixed asset depreciation, accounting accruals, and the immediate expensing of intangible assets.

Addressing Program Sustainability: A Time Series Analysis of the Supplemental Nutrition Assistance Program in the US

Food stamps, or, more formally, the Supplemental Nutrition Assistance Program (SNAP), form a crucial support system for many individuals in the United States. The welfare benefit has been associated with improved nutritional outcomes, making it an effective strategy to address hunger. Additionally, it has been found to enhance labour market outcomes for recipients and contribute to higher birth weights among children born to SNAP recipient mothers. Furthermore, it has been linked to improved height and overall health outcomes. Overall, this benefit is a crucial resource during times of need and helps mitigate the adverse effects of economic fluctuations. Even with the manifold advantages of the program, there exist notable apprehensions about the program’s long-term viability. SNAP has experienced significant expansion over more than 50 years. The recipients grew more than 14 times, from approximately 2.9 million individuals in 1969 to over 41 million in 2022. This study utilizes data from the United States Department of Agriculture to investigate the temporal characteristics of this massive expansion. Augmented Dickey-Fuller tests are performed with and without trend and optimally selected lag lengths. In all specifications, the presence of a unit root cannot be rejected. An overwhelming body of evidence suggests that the growth in SNAP beneficiaries may need to be revised. Results of the unit root tests provide credence to the argument that the historical growth rate in the number of SNAP beneficiaries is highly unpredictable and may pose significant challenges to policymakers. This study does not attempt to calculate fraud and abuse in the program. Neither does the study attempt to ascertain the number of beneficiaries that may not be worthy of receiving the benefits.

Deep decarbonization and U.S. biofuels production: a coordinated analysis with a detailed structural model and an integrated multisectoral model

Scenarios for deep decarbonization involve biomass for biofuels, biopower, and bioproducts, and they often include negative emissions via carbon capture and storage or utilization. However, critical questions remain about the feasibility of rapid growth to high levels of biomass utilization, given biomass and land availability as well as historical growth rates of the biofuel industry. We address these questions through a unique coordinated analysis and comparison of carbon pricing effects on biomass utilization growth in the United States using a multisectoral integrated assessment model, the Global Change Analysis Model (GCAM), and a biomass-to-biofuels system dynamics model, the Bioenergy Scenario Model (BSM). We harmonized and varied key factors—such as carbon prices, vehicle electrification, and arable land availability—in the two models. We varied the rate of biorefinery construction, the fungibility of feedstock types across conversion processes, and policy incentives in BSM. The rate of growth in biomass deployment under a carbon price in both models is within the range of current literature. However, the reallocation of land to biomass feedstocks would need to overcome bottlenecks to achieve growth consistent with deep decarbonization scenarios. Investments as a result of near-term policy incentives can develop technology and expand capacity—reducing costs, enabling flexibility in feedstock use, and improving stability—but if biomass demand is high, these investments might not overcome land reallocation bottlenecks. Biomass utilization for deep decarbonization relies on extraordinary growth in biomass availability and industrial capacity. In this paper, we quantify and describe the potential challenges of this rapid change.

Modeling of the Spatial Distribution of Forest Carbon Storage in a Tropical/Subtropical Island with Multiple Ecozones.

Visual data on the geographic distribution of carbon storage help policy makers to formulate countermeasures for global warming. However, Taiwan, as an island showing diversity in climate and topography, had lacked valid visual data on the distribution of forest carbon storage between the last two forest surveys (1993-2015). This study established a model to estimate and illustrate the distribution of forest carbon storage. This model uses land use, stand morphology, and carbon conversion coefficient databases accordingly for 51 types of major forests in Taiwan. An estimation in 2006 was conducted and shows an overall carbon storage of 165.65 Mt C, with forest carbon storage per unit area of 71.56 t C ha-1, where natural forests and plantations respectively contributed 114.15 Mt C (68.9%) and 51.50 Mt C (31.1%). By assuming no change in land use type, the carbon sequestration from 2006 to 2007 by the 51 forest types was estimated to be 5.21 Mt C yr-1 using historical tree growth and mortality rates. The result reflects the reality of the land use status and the events of coverage shifting with time by combining the two forest surveys in Taiwan.

Studying beef production evolution to plan for ecological intensification of grazing ecosystems

CONTEXTA challenge facing the livestock sector is improving beef production while mitigating negative environmental impacts. Analyzing its past productive and environmental performance may elucidate strategies for improving efficiency of grassland-based systems and identify future research and public policy priorities. OBJECTIVESDescribe past and current dynamics and assess potential future scenarios of the Uruguayan beef production sector, considering historical beef production growth rate (kg carcass), partitioning in land use by the cattle sector (ha), cattle animal performance (kg carcass animal−1 year−1) and stocking rate (animal ha−1) since 1966. METHODSWe quantified drivers of beef production and total enteric methane (CH4) emissions and modeled the gap between their current and potential levels by applying new management practices at country scale. Potential systems included reduced age of replacement heifers at first pregnancy and greater cow weaning rates. Results AND CONCLUSIONSCattle land area expansion and increased animal performance explained most of beef production growth (33 and 52%, respectively), while stocking rate had a negligible effect. We identified that recent beef production was driven by improved pasture area (R2 = 0.42), which represented only 21% of cattle area. This disproportionate effect showed a dependency of national beef production on improved pastures. The current weaning rate (65%) and the number of replacement heifers that have first pregnancy by 36 months of age (480,000 heads) revealed a national inefficiency, falling short of the potential of grazing systems based on native pastures. When concepts and principles of ecological intensification of native pasture grazing systems are applied optimally, weaning rate can be increased from 65 to 85% and age of first pregnancy reduced from 36 to 24 months, decreasing national enteric CH4 emissions by 13%. The weaknesses identified in the national beef production sector support re-focusing research, public policy, and technologies applied by farmers to shift the paradigm of beef production to greater ecological intensification based on native pastures. In this way, it will be possible to increase beef production and mitigate environmental impacts of grazing systems. SIGNIFICANCEWe demonstrate that re-focusing beef production from improved pastures to native pastures, while enhancing their management, will mitigate the CH4 emissions of the beef sector at large scales and will underpin the annual beef production, leading to a stable annual production, reducing the dependency on improved pasture area and providing ecosystem services.

Maternal Transfer and Long-Term Population Effects of PCBs in Baltic Grey Seals Using a New Toxicokinetic–Toxicodynamic Population Model

Empirical evidence has shown that historical exposure of polychlorinated biphenyls (PCBs) to Baltic grey seals not only severely affected individual fitness, but also population growth rates and most likely caused the retarded recovery rate of the depleted population for decades. We constructed a new model which we term a toxicokinetic–toxicodynamic (TKTD) population model to quantify these effects. The toxicokinetic sub-model describes in detail the bioaccumulation, elimination and vertical transfer from mother to offspring of PCBs and is linked to a toxicodynamic model for estimation of PCB-related damage, hazard and stress impacts on fertility and survival rates. Both sub-models were linked to a Leslie matrix population model to calculate changes in population growth rate and age structure, given different rates of PCB exposure. Toxicodynamic model parameters related to reproductive organ lesions were calibrated using published historical data on observed pregnancy rates in Baltic grey seal females. Compared to empirical data, the TKTD population model described well the age-specific bioaccumulation pattern of PCBs in Baltic grey seals, and thus, the toxicokinetic parameters, deduced from the literature, are believed to be reliable. The model also captured well the general effects of PCBs on historical population growth rates. The model showed that reduced fertility due to increased PCB exposure causes decreased vertical transfer from mother to offspring and in turn increased biomagnification in non-breeding females. The developed TKTD model can be used to perform population viability analyses of Baltic grey seals with multiple stressors, also including by-catches and different hunting regimes. The model can also be extended to other marine mammals and other contaminants by adjustments of model parameters and thus provides a test bed in silico for new substances.

Is Expected Inflation the Best Long-term Sustainable Growth Rate?

When using the income approach to value a business, selecting a sustainable long-term growth rate is a necessary input into the valuation model, one that can have a material effect on the value of the business. The business valuation literature has provided little guidance for estimating and understanding sustainable long-term growth rates. Apart from relying on macroeconomic indicators and historical industry averages, appraisers have few options for selecting sustainable long-term growth rates and are often relegated to making arbitrary assumptions. The objective of this article is to examine the accuracy of using historical industry average growth rates, historical average gross domestic product growth rates, and expected inflation as sustainable long-term growth rates. The accuracy of the forecasts was examined vis-à-vis actual growth rates for twenty-one selected industries from 1991 to 2021 for the purpose of selecting the most accurate forecasting method. The forecasts premised on expected inflation proved to be the most accurate. In light of the results, the notion that expected inflation may be the best estimate of sustainable long-term growth was explored.

From the Editor

Sustainable growth is one of the key inputs in the income approach. I have seen wide variation in practice over time with respect to selection of a sustainable growth rate ranging from long-term targets for inflation, expected industry growth rates, T-Bond yields and expected growth of the Gross Domestic Product. This list is clearly not exhaustive; I have seen many different variations with high growth companies. Shannon Pratt illustrated the significance of the long-term growth rate in the following passage from Valuing a Business:As the example illustrates, minor differences in sustainable growth rates can have very significant impacts on an opinion of value. This issue contains two papers that directly address the issue of sustainable growth. Dr. Gene Trevino analyzes the issue from the standpoint of the accuracy of using historical industry growth rates, historical gross domestic product growth rates and expected inflation rates as sustainable long-term growth rates, while Dr. Brad Cornell and Richard Gerger approach the topic by analyzing historic growth of public companies. Their findings are surprisingly similar.David Neuzil and Joe Thompson revisit the Value Driver Formula and illustrate short-comings of a traditional Gordon Growth Model in valuing a business that invest heavily in research and development.Finally, the issue has a Special Report from the International Valuation Standard Council (“IVSC”) on Environmental, Social and Governance (“ESG”) issues in valuation. The IVSC has been busy developing technical resources for valuation professional in the evolving area of ESG and its application in business valuation. The Special Report provides an overview of those resources and ongoing work in the area.To all our readers, enjoy the issue and let me know if you have any strong opinion or further thoughts on our articles – letters to the editor are welcome.

Assessment of current developments and future prospects of wind energy in Canada

The negative consequences of global warming and climate change demonstrate that conversion to carbon-free or low-carbon, sustainable, and eco-friendly energy sources is essential. Wind energy substitution, as a renewable and widely available energy source, can be an appropriate, effective, and practical contribution to reducing fossil fuel use worldwide. To show the importance of wind energy in the future global energy supply, the current status and future prospects of wind power in Canada are statistically analyzed, as a case study, in terms of installed capacity, generation, capacity factor, and insights are provided on the levelized cost of energy (LCOE). The originality of this study is to present a comprehensive basis for assessing future growth and the potential development of wind power in Canada’s electricity supply mix to the year 2040 by using different predictive methods, including historical compound annual growth rate (CAGR), linear forecasting, and CAGR of 5, 10 and 15%, selected based on a presumed policy interjection at the provincial- or territorial-level. The comparative assessment of wind energy generated by various predictive methods up to 2040 shows that the historical CAGR and the CAGR of 15% respectively have the lowest and highest growth rates in meeting the total annual end-use electricity demand for Canadian provinces and territories, except for Alberta, Saskatchewan, Yukon, and Nunavut, where the lowest growth rate belongs to the linear forecasting approach. However, these predictive scenarios strongly depend on the energy policies of the provincial or territorial governments. In addition, the influence of increasing installed wind power capacity in Canada on the LCOE is examined using a linear forecasting model with various decline rates for costs. The results indicate that the reduction in LCOE robustly depends on a higher installed wind power capacity. Moreover, the relationship between the wind power capacity factor and LCOE is evaluated based on different forecasting scenarios, where it is observed that the LCOE is strongly inversely associated with capacity factor. As a general result, many regions of Canada are identified as having a highly promising wind energy potential, especially in the Gulf of St. Lawrence, either as a stand-alone source or integrated with other energy sources, along with energy storage capabilities, to meet future electricity requirements with a low carbon footprint.

Biographies and Bodies of Pupils of the Amsterdam Maritime Institute, 1792–1943

Abstract In this article, the authors describe and explore the dataset Pupils of the Amsterdam Maritime Institute 1792–1943, which is based on the Comportementboeken of the Amsterdam Maritime Institute. These records contain biographical information and bodily measurements of aspirant sailors between 12 and 20 years of age. The authors have linked the records (N = 5439) to enrolment records and the examinations for the military draft, which provides unique data on historical adolescent growth rates. Apart from anthropometric research, the dataset can be used for different kinds of studies into the background and early careers of Dutch sailors.

The predictive content of public debt for real output expansions and contractions over three centuries: A Markov switching analysis for the UK

This paper examines the relationship between public debt and real output in the UK during the last three centuries to identify changes in the historical growth rates of real output, to understand the asymmetric behavior over real output expansions and contractions, and to consider whether the growth rate of the debt-to-GDP ratio is a leading indicator of the time-varying transition probabilities between expansions and contractions. The Markov switching analysis showed that changes in the debt-to-GDP ratio have predictive content for real output expansions with a negative sign and constitute a leading indicator of the transition probabilities across the two regimes.

Nonprofits and Pass-Throughs: Performance Comparison

This paper’s purpose is to compare nonprofits with pass-throughs in terms of valuation, leverage, and growth. To achieve this purpose, we use the Capital Structure Model. This model determines maximum firm valuation through incorporating real data (tax rates, credit spreads, and historical growth rates). Since this is the first study to offer our particular set results on valuation, leverage and growth, our findings are value-additive in terms of the comparative research on nonprofits and pass-throughs. The new and scientific value of our findings are further established by robust tests that modify values for key variables. Major findings include the following. Nonprofits have over a fifty percent valuation advantage over pass-throughs and achieve a four times greater increase in dollar value when going from nongrowth to growth. The latter accomplishments are attained with a smaller before-tax plowback ratio and less retained earnings. Such achievements occur because nonprofits are not taxed on earnings retained for growth. While nonprofits have somewhat greater optimal leverage ratios than pass-throughs, they gain a bit less in dollars added from debt unless growth rates increase as projected when tax rates are lowered. Nonprofits gain less percentage-wise from debt because their unlevered firm value is greater than pass-throughs.

Дослідження практики зниження ризиків міжнародного трансферу технологій в умовах загострення конкурентної боротьби

Analysis of at the global energy market, according to the Advanced Energy Now 2019 Market Report, showed that the global electricity transmission and distribution market demonstrated the highest historical aggregate annual growth rates (CAGRs) between 2011 and 2019. A huge number of small and medium companies got involved into it and into transfer technology process generally.

Florida Key Deer Abundance and Recovery Following New World Screwworm Infestation

An infestation of Cochliomyia hominivorax (New World Screwworm, hereafter Screwworm) was detected in the endangered Odocoileus virginianus clavium (Florida Key Deer) population in July 2016. We assessed the impact of this infestation on Florida Key Deer population abundance and recovery potential. We synthesized historical mortality and population data with new analyses including monitoring of Florida Key Deer mortalities and estimation of abundance on Big Pine Key and No Name Key (islands that support 75% of the Florida Key Deer population). We documented 135 Screwworm-related Florida Key Deer mortalities (∼9–20% of the total population) during the Screwworm outbreak (July 2016–January 2017). Most mortalities occurred in the adult male population, as Screwworm flies laid eggs on open wounds sustained from sparring during mating season. The Screwworm incident was contained prior to the 2017 fawning season, which prevented substantial negative impacts on females or fawns. Historical growth rates at similar population levels and sex ratios indicated that, absent other external variables (e.g., Hurricane Irma in September 2017), the population was likely to recover.

Practical Applications of The Unrealistic Optimism That Threatens Retirement Security

<h3>Practical Applications Summary</h3> In <b><i>The Unrealistic Optimism That Threatens Retirement Security</i></b>, from the Fall 2019 issue of <b><i>The Journal of Retirement</i></b>, author <b>Alfred Rappaport</b> of <b>Northwestern University</b> details the retirement crisis facing Americans today. Fewer employers are offering lifetime pensions, and nearly half of American families have no retirement savings—but most Americans believe they are on track to a comfortable retirement. This unrealistic optimism comes from the fact that Americans underestimate their expenses in retirement while overestimating how much their investments will grow once they start saving. Investors often assume they will realize high rates of return in line with historical growth rates. However, they often sabotage their retirement nest egg’s potential by failing to reinvest dividends, and by investing when the market is high and selling when the market is low. Investors also often fail to factor in investment fees, inflation, and taxes. The author gives investors a reality check, providing tools that show how these factors cut into retirement savings over time. Financial advisors can use these tools to educate their clients and create retirement savings plans that are free of unrealistic optimism. <b>TOPICS:</b>Wealth management, retirement, pension funds, portfolio theory

What drives urban population growth and shrinkage in postsocialist East Germany?

AbstractMost cities in East Germany experienced drastic shrinkage between 1990 and 2014, but in recent years some have stabilized or even switched toward new growth paths, while others have been continuously declining. A descriptive analysis of subgroups of cities with diverging population growth trajectories reveals that this recent phase of “urban resurgence” accounts to a large share to the growth of the largest cities and some smaller cities in their hinterland and is strongly related to the residential preferences of families and young adults. Furthermore, the estimation of spatial regression models for the period 2004–2014 including a comprehensive set of control variables, such as agglomeration effects, initial demographic conditions, labor market characteristics, climate, and quality of life‐related factors, reveals that recent patterns of urban population development are negatively correlated to historical growth rates during times of socialism. This indicates that postsocialist urban development can be interpreted as a direct outcome of the transition process and as a backlash against the socialist past when urban development was dominated by central planning policies. However, the relevance of the individual determinants included in our regression model differs strongly with respect to the population dynamics in different age groups.

Picea (spruce) growth rate (mm year−1) changes in southwest Yukon (Canada) since the mid 19th century

Analyses of maximum early tree ring growth within a regional curve standardization (RCS) framework were used to construct ring width chronologies in mm year−1 to estimate historical growth rates in southwest Yukon (Canada), without the necessity of correcting for age related changes. The analysis relied on the use of the 11th–50th year growth rings to standardize chronology comparisons. This age range represents the period of peak radial growth for Picea albertiana and P. glauca (white spruces), which are common trees on upland boreal sites in the study area. Of 1,460 available Picea ring series, 1,029 fit the criteria for inclusion in the study and they represented five commonly occurring RCS curve types. When converted to chronologies, all curve types had year-to-year fluctuations in average ring widths (RW11–50), but nine of the 12 RCS types (four types in three study areas) also had long-term growth rate increases of 15–132% between ad 1850 and 2000, based on regression modeling (R/r = 0.54–0.76, p 0.94, n > 100) with conventional tree ring width index values derived from multiple RCS curves. This indicates that the two chronology construction approaches have very similar temporal patterns despite the maximum early growth analysis technique representing ring widths in mm.